QPPT: Query Processing on Prefix Trees

Modern database systems have to process huge amounts of data and should provide results with low latency at the same time. To achieve this, data is nowadays typically hold completely in main memory, to benefit of its high bandwidth and low access latency that could never be reached with disks. Current in-memory databases are usually columnstores that exchange columns or vectors between operators and suer from a high tuple reconstruction overhead. In this paper, we present the indexed table-at-a-time processing model that makes indexes the first-class citizen of the database system. The processing model comprises the concepts of intermediate indexed tables and cooperative operators, which make indexes the common data exchange format between plan operators. To keep the intermediate index materialization costs low, we employ optimized prefix trees that oer a balanced read/write performance. The indexed tableat-a-time processing model allows the ecient construction of composed operators like the multi-way-select-join-group. Such operators speed up the processing of complex OLAP queries so that our approach outperforms state-of-the-art in-memory databases.